Strategies for adaptive optimization with aggregation constraints using interior-point methods

Constraint-aggregation methods are used in engineering optimization problems to approximately impose a bound on a quantity of interest in a differentiable manner. In this paper, we present strategies to adaptively solve aggregation-constrained problems. These adaptive techniques achieve a tighter bound approximation while also reducing the computational cost of optimization. We focus on two aggregation techniques: Kreisselmeier–Steinhauser (KS) aggregation, and induced exponential aggregation. We demonstrate that the proposed adaptive technique achieves significant computational savings compared to fixed-aggregation methods for a series of stress-constrained mass-minimization problems.